New drug development is typically motivated by the need or opportunity to affect an individual's quality of life. Development focuses on identifying and selecting compounds having the potential to affect one or more mechanisms thought to be critical in altering specific clinical aspects of the disease processes.
Drug development is also motivated by exciting research data regarding cellular and subcellular phenomena. Very often, however, the data considers only an isolated and rather narrow view of an entire system. Such data may not provide an integrated view of the complete biological system. Moreover, the narrow findings reported are not always entirely accurate when translated to the whole body level.
Current methods of obtaining data for biological processes require extremely time consuming laboratory experiments that lead to animal experiments and clinical trials. From these trials and experiments, data are obtained which usually focus on a very narrow part of the biological system. While conclusions may be drawn by assimilating experimental data and published information, it is difficult, if not impossible, to synthesize the relationships among all the available data and knowledge. In fact, the human mind is only capable of considering approximately seven factors at one time, and lacks the ability to accurately account for feedback in systems over time. Furthermore, incorporation of multiple uncertainties, as well as feedback, often leads to oversimplification or artificial partitioning by the human mind, which can result in misleading conclusions.
Previous modelling efforts for designing drugs have typically focused on creating molecular models of a proposed drug or drug target. The molecular models are designed to meet certain criteria believed to have a desired impact at the molecular level. The desired impact is generally determined by studying the biology of interest at the molecular level through laboratory experiments.
Drugs designed using this type of modelling either represent refinements of existing drugs or an attempt to develop a drug for a new part of the disease that was suggested from conclusions drawn from clinical trials and laboratory experiments. The complexity of the information, however, does not always provide a clear and consistent picture from which accurate conclusions can be drawn, and the resulting designer drugs often reflect this inaccuracy.
Typically, designer drugs often meet design goals related to particular conclusions and observations at a cellular or subcellular level, but may fail when clinically tested because the design process fails to take into account the nuances of the complete biological system. Only after numerous costly trial-and-error clinical trials, and constant redesigning of the clinical use of the drug to account for lessons learned from the most recent clinical trial, is a drug having adequate safety and efficacy finally realized. This process of clinical trial design and redesign, multiple clinical trials and, in some situations, multiple drug redesigns requires great expense of time and money. Even then, the effort may not produce a marketable drug.
This scenario has a chilling effect on efforts to produce a drug for anything but an extremely large segment of the population. Biological abnormalities which may be treatable by a drug may not be explored because the potential market for the drug does not justify the expenditure of resources necessary to design, test, and obtain approval for the drug.
Because of the high initial costs of clinical trials, experimentation, and government approval, drug development today focuses on large patient populations. Even then, development is extremely speculative. In summary, the overhead for drug development is very high, and difficult to justify except for the largest of patient populations.
Clinical trials typically are designed to isolate on a single variable, and use a placebo control group as a baseline from which the variable is measured. Observations from a clinical trial attempt to draw conclusions from apparent differences between the control group and the experimental group. These observations, however, do not take into account the multi-variable dynamic nature of the patients individually, or as a group. Such variations usually increase the variability in the data and require large test populations to deal with the variability in an appropriate statistical manner.
A typical cycle for a clinical trial can require years; designing the trial may take six months, performance of the trial may take a year, and analysis of the results may take yet another six months. After years of testing, the results still may be subject to suspicion. Additionally, a trial may be one of several ongoing trials necessary to address the variables associated with a particular area of investigation.
Due to the single-variable nature of the drug development business, the reported data results in a great degree of uncertainty. Each study provides a very narrow, often debatable, view of the complete system. Ultimately, the different studies fail to provide a complete picture of the entire biological system, since the studies develop information from different perspectives and assumptions.
What is needed then, is an alternative system and method which efficiently discovers and conveys information regarding complex biological systems.